Risk prediction equations are widely used in cardiovascular medicine for risk stratification and to determine cost-effective and appropriate courses of treatment. Whether new predictors can add clinical utility to established models such as the Framingham risk score is an important question. Previous work by these investigators has introduced new methods for comparing risk prediction models, including risk reclassification, which assesses the ability of new models to more accurately classify individuals into higher or lower risk strata. This proposal offers several novel extensions of these methods, particularly related to calibration, which directly compares the predicted to the observed risk. Such calibration is essential for estimating risk for the individual and computing differences in absolute risk. While measures of improvement in discrimination are available, this application proposes an integrated approach examining improvement in calibration. Also, since pre-specified risk strata are not identified for all applications, a category-free measure of reclassification calibration is proposed. A second area of interest is extending methods for reclassification calibration to other study designs. While methods for survival data are available for discrimination measures, how well measures of calibration, particularly the reclassification chi-square statistic, extend to survival settings is not yet known. In addition, case-cohort and matched case-control studies are widely used in cardiovascular research, especially for biomarker assessment. It is not known how well reclassification measures translate to these designs. Finally, to limit costs, clinicians are often interested in multi-stage screening of diseae. Reclassification of those at intermediate risk is of most interest, and the 'clinical NRI'has been introduced to assess reclassification in this group. How well this new method performs in practice remains to be determined. We propose to conduct a portfolio of research projects regarding the comparison of predictive models in general, and reclassification calibration specifically, that will further the development of these methods and their clinical utility. We wil examine the characteristics of the new measures in data on cardiovascular disease from the Women's Health Study, in simulations, and in existing case-control and case-cohort data on CVD outcomes. Extensions to these novel applications would be highly innovative and directly and immediately applicable to clinical risk prediction for cardiovascular disease.

Public Health Relevance

Risk prediction equations are widely used in cardiovascular medicine for risk stratification and to determine cost-effective and appropriate courses of treatment. This project proposes to develop new methods for comparison of risk prediction models, particularly related to reclassification calibration. It will extend these methods to other study designs and settings other than binary outcomes.